2009. 8. 18. 21:29
Computer Vision
Nistér, D. 2004. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. Pattern Anal. Mach. Intell. 26, 6 (Jun. 2004), 756-777. DOI= http://dx.doi.org/10.1109/TPAMI.2004.17
An efficient solution to the five-point relative pose problem
Nister, D.
Sarnoff Corp., Princeton, NJ, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: June 2004
Volume: 26, Issue: 6
On page(s): 756-770 An Efficient Solution to the Five-Point Relative Pose Problem
David Nist´er
Sarnoff Corporation
Center for Visualization and Virtual Environments and the Computer Science Department of University of Kentucky
H. Stewenius, C. Engels, and D. Niste. Recent developments on direct relative orientation.
ISPRS Journal of Photogrammetry and Remote Sensing, 60:284-294, June 2006.
Calibrated Fivepoint Solver
http://www.vis.uky.edu/~dnister/Executables/RelativeOrientation/
Dhruv Batra, Bart Nabbe, and Martial Hebert. An Alternative Formulation for the Five Point Relative Pose Problem. IEEE Workshop on Motion and Video Computing 2007 (WMVC '07).
http://www.ece.cmu.edu/~dbatra/research/fivept/fivept.html
Five-Point Motion Estimation Made Easy
Hongdong Li and Richard Hartley (RSISE, The Australian National University. Canberra Research Labs, National ICT Australia.)
preview
SfM = structure from motion
http://en.wikipedia.org/wiki/Structure_from_motion
eight-point algorithm
http://en.wikipedia.org/wiki/Eight-point_algorithm
algorithm used in computer vision to estimate the essential matrix or the fundamental matrix related to a stereo camera pair from a set of corresponding image points
Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in computer vision. Cambridge University Press.
http://www.robots.ox.ac.uk/~vgg/hzbook/
ch.8 - ch.10
Richard I. Hartley (June 1997). "In Defense of the Eight-Point Algorithm". IEEE Transaction on Pattern Recognition and Machine Intelligence 19 (6): 580–593. doi:.
Richard Szeliski, Microsoft Research
Computer Vision: Algorithms and Applications
ch.7 Structure from Motion
Epipolar geometry - Fundamental matrix
An efficient solution to the five-point relative pose problem
Nister, D.
Sarnoff Corp., Princeton, NJ, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: June 2004
Volume: 26, Issue: 6
On page(s): 756-770 An Efficient Solution to the Five-Point Relative Pose Problem
nisterd_efficient.pdfDavid Nist´er
Sarnoff Corporation
Center for Visualization and Virtual Environments and the Computer Science Department of University of Kentucky
H. Stewenius, C. Engels, and D. Niste. Recent developments on direct relative orientation.
ISPRS Journal of Photogrammetry and Remote Sensing, 60:284-294, June 2006.
Calibrated Fivepoint Solver
http://www.vis.uky.edu/~dnister/Executables/RelativeOrientation/
Dhruv Batra, Bart Nabbe, and Martial Hebert. An Alternative Formulation for the Five Point Relative Pose Problem. IEEE Workshop on Motion and Video Computing 2007 (WMVC '07).
http://www.ece.cmu.edu/~dbatra/research/fivept/fivept.html
Five-Point Motion Estimation Made Easy
Hongdong Li and Richard Hartley (RSISE, The Australian National University. Canberra Research Labs, National ICT Australia.)
preview
SfM = structure from motion
http://en.wikipedia.org/wiki/Structure_from_motion
eight-point algorithm
http://en.wikipedia.org/wiki/Eight-point_algorithm
algorithm used in computer vision to estimate the essential matrix or the fundamental matrix related to a stereo camera pair from a set of corresponding image points
Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in computer vision. Cambridge University Press.
http://www.robots.ox.ac.uk/~vgg/hzbook/
ch.8 - ch.10
Richard I. Hartley (June 1997). "In Defense of the Eight-Point Algorithm". IEEE Transaction on Pattern Recognition and Machine Intelligence 19 (6): 580–593. doi:.
Richard Szeliski, Microsoft Research
Computer Vision: Algorithms and Applications
ch.7 Structure from Motion
Epipolar geometry - Fundamental matrix
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